ABSTRACT:

Classification o f soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain oriented decisions often depend on the type of soil like farmers might be benefitted from knowing t he kind o f s oil t o choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, VGG16, and CNN2D. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper.

Index Terms—Convolution Neural Network, Soil classification, Supervised learning, Transfer learning

Problem Statement:

Accurate soil classification is crucial for applications in agriculture, environmental monitoring, and land management. Traditional soil classification methods often rely on manual examination, which can be time-consuming, labor-intensive, and prone to human error. With the increasing availability of image data, there is a need for automated methods that can classify soil types efficiently and accurately based on visual characteristics.This project aims to address the challenge of automatically classifying soil images using Convolutional Neural Networks (CNNs). CNNs, known for their powerful feature extraction capabilities, will be employed to analyze and distinguish soil types from image data. The goal is to develop a robust and scalable model that can accurately classify different soil types based on their visual patterns, textures, and colors, improving the speed and precision of soil classification processes. The solution must handle variability in soil appearance, environmental conditions, and dataset diversity, providing an efficient tool for stakeholders in agriculture and environmental sciences.

Objective:

The objective of this project is to develop an efficient and accurate classification model for soil images using Convolutional Neural Networks (CNNs). The model aims to automatically identify and classify different soil types based on their visual characteristics, such as texture, color, and patterns, by leveraging the feature extraction capabilities of CNNs. This classification system will assist in providing rapid, reliable, and automated soil type identification, which is critical for various applications in agriculture, environmental management, and geosciences. The system will focus on enhancing classification accuracy while ensuring scalability and robustness when applied to large and diverse datasets.